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All JupyterHub sessions running on this service are time limited. When the time is up, the instance will be killed automatically along with any active processing that may be taking place. You can only have one session running. If you left one running, JupyterHub will connect you straight back into it. If the service is busy and there are no resources available, you will be informed as such and will need to try again later. In the the early stages of the service we will closely monitor usage, and modify options to ensure that we provide a fair usage to all our users. |
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This is the general ECMWF JupyterHub launcher, therefore it is possible that you have access to more than the Data Store option described here |
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DSS users will be able to spawn sessions with the environment summarised in the table below. This can be selected from the "Select an Environment" dropdown selector on the JupyterHub Launcher. Please note that additional environment options may be added to this list as the service evolves to meet the needs of users.
Name | Use case | RAM | CPUCPUs | Duration |
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ECMWF Data Store Service | Some small data processing, e.g. data averaging of small files | 4 Gb | 2 cores | 5 hours |
For reference, a month for one variable in the ERA5 hourly data on single levels is roughly 1.5 Gb. Larger volumes of data could be computed if using block-wise processing of data, e.g. using dask chunks in xarray.
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Each user will have a "home" storage allocation (see table above for size). If you do not use the JupyterHub service for a period of 31 days the private storage will be removed. This storage is only accessible to you.
We The DSS service does not provide any back-up for the data stored, therefore we strongly advise that you use git repositories to back up any files stored in the private storage such that you can . This could be used recreate any work should your private storage be removed. JupyterHub provides a git plugin which makes it simple to clone your repository.
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Each user will have an allocated quota on the temporary scratch disk (see table above for size). If you exceed the maximum quota, a clean up script will will irreversibly remove your largest oldest files (by modified time). Any attempt to cirumvent this behaviour is considered malicious and will lead to your access to JupyterHub being revoked.
The scratch disk is a shared resource and is cleaned regularly. When the shared usage of all users exceeds the maximum quote, the files modified least recently will be removed. This means that files stored here should not be considered permanently stored, they should exist for your current session and may or may not be there when you return. The lifetime of these files will depend on the general usage of the service, and at this stage it is not possible to provide an expected lifetime of such files.
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